The present application encompasses proteins and peptide fragments of those proteins, which are produced by proteolytic digestion of the proteins, and which both proteins and peptide fragments are useful for diagnosing of cancer or for monitoring for the presence of cancer in an individual.
Screening mammograms typically have a sensitivity of 75% and specificity of around 98% resulting in a false positive rate of roughly 5% per mammogram (Brown, Houn, Sickles, & Kessler, 1995; Kolb, Lichy, & Newhouse, 2002; Luftner & Possinger, 2002). Breast tissue type, more specifically density, also greatly influences the performance of mammography. The degree of breast density is classified using the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS). This system consists of four classifications 1-4; where category 1 is mostly fatty (<25% dense); category 2 is scattered fibroglandular densities (25-50% dense); category 3 is heterogeneously dense (51-75% dense) and category 4 is extremely dense (>75% dense) (Bigenwald, 2008; Klifa, 2010; Scheel, 2014).
For women with fatty breast tissue, mammography can be an effective screening tool, when patients are compliant with yearly screenings (Tabar, 2001; Pisano, 2005). However, as breast density increases, the effectiveness of mammography decreases leading to increased follow up imaging and, more importantly, missed cancer diagnosis. Mammographic sensitivity, for high-risk patients, has been shown to be as low as 31% for category 1, 27% for category 2, 20% for category 3, and 12.5% for category 4 (Bigenwald, 2008). Approximately fifty percent of the female population is in categories 3 and 4, which is considered dense breast tissue (Vachon, 2007). Currently, the best screening option for these women is MRI, which can be up to 10 times more expensive than mammography (Beignwald, 2008). Lack of good screening options is a serious problem as women with 75% or more dense tissue have four to six times greater risk of developing breast cancer than women with less dense tissue (Boyd, 2007).
Follow up imaging to evaluate false positives costs the US over 4 billion dollars with an additional 1.6 billion dollars spent for biopsies alone. In 2010 of the 1.6 million biopsies performed, as few as 16% (only 261,000) were found to have cancer (Grady, 2012). The answer to increasing the diagnostic parameters of imaging can be found in the pre- and post-image diagnostics that focus on genetic and proteomic information, more specifically, biomarkers (Armstrong, Handorf, Chen, & Bristol Demeter, 2013; Li, Zhang, Rosenzweig, Wang, & Chan, 2002).
Tissue and serum are commonly the most logical place for beginning biomarker research, however the large dynamic range of both mediums makes discovery quite difficult (Schiess, Wollscheid, & Aebersold, 2009). The answers may lie in less complex biological fluids, such as saliva and tears. The use of tears as diagnostic medium is not a novel application as the tear proteome has been extensively investigated previously (Böhm et al., 2012; 2011; Lebrecht, Boehm, Schmidt, Koelbl, & Grus, 2009a; Lebrecht et al., 2009b; Wu & Zhang, 2007). In this application a quantitative assay for the detection of a panel of tear-based biomarkers in response to cancer is disclosed. From this quantitative information, the framework for a Certified Laboratory Improvement Amendments (CLIA) protocol will be defined.